The present invention generally relates to systems and methods for power generation management, and more particularly, a method and system for determining a generator dispatch plan for electrical networks which achieves the lowest total expected costs to meet the demand under uncertainty on the demand and/or generation.
Modeling uncertainty in dispatching energy is becoming more critical as renewable energy technologies play an increasing role in the portfolio mix of electricity generation.
FIG. 1 provides an illustration. The power flow between each pair of nodes obeys certain non-linear equations that arise out of Kirchhoff's law. An economic dispatch (ED) problem or optimal power flow (OPF) problem is said to have been solved when the dispatch and transmission decisions are taken to minimize the total cost of generation. A linearized version of the alternate current (AC) power flow equations, called the direct current (DC) power flow equations, can be used to gain tractability at the expense of optimality.
Forecasting near-term wind availability and velocity is an imperfect science. Assessing study how wind power production can be integrated into existing dispatch models, and analyze the impact of uncertainty of forecast errors for wind power production on incremental reserve requirements and imbalance costs.
Entities in charge of smooth operation of the electrical grid require that this uncertainty associated with utilizing renewable sources such as wind power must be hedged. Various approaches have been proposed to tackle this: for example, presenting a balancing algorithm to achieve overall dispatch-ability in a distributed generation network by actively managing a group of small distributed generations to convert them into one large more controllable logical generation station.
Non-linear power balance is a routine operational constraint for energy transmission and distribution companies, and ability to incorporate uncertainty could provide large cost savings in introducing intermittent sources of supply like renewables.
Such problems are generally formulated as a very large-scale nonlinear, nonconvex optimization problem, which requires a solution in near real-time. The existing methods have problems with handling the non-linear, non-convex issue and uncertainty. They are often only able to solve for linearized approximation formulations or use the sampling-based algorithms for the nonlinear one, whose identified solutions have no guarantees on optimality.
It would be highly desirable to provide a system and method for balancing energy provided by non-dispatchable sources (such as wind and photovoltaic units) with quickly dispatchable, sources (such as small hydro and micro turbine units).